This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The past decade has revealed the breadth of RNA's functions, which include information storage, catalysis, and cellular regulation. However, computational approaches to RNA structure have lagged behind those involving proteins. We propose two computational projects that will allow us to produce quality models of RNA structure. We have developed a high resolution RNA force field that has been included in the Rosetta modeling software [1]. Our first goal is to develop and refine novel conformational search algorithms using this force field. However, to validate such procedures requires training and benchmarking on a wide array of existing structures. Such a benchmark will be possible only by scaling up to a larger supercomputing environment. Secondly, we plan to model the dynamics of small RNA systems using CUDA-optimized molecular dynamics code (OpenMM). The performance gains (5x-100x) enabled by GPGPU technology will allow the simulation of RNA folding at atomic resolution. In the past, such computations have been limited to distributed computing environments such as Folding@Home. The resources afforded by the Teragrid infrastructure will allow us to scale up our research both in scope and in speed. We expect that the quick turnaround time afforded by TeraGrid resources will allow a more dynamic relationship between theory and experiment: quantitative predictions will be quickly verified in our lab by high throughput structure mapping approaches that achieve single residue resolution [2]. [1] Das, R. and Baker, D. (2007) "Automated de novo prediction of native-like RNA tertiary structures", Proceedings of the National Academy of Sciences U.S.A. 104, 14644"14669. [2] Das, R., Kudaravalli, M., Jonikas, M., Laederach, A., Fong, R., Schwans, J.P., Baker, D., Piccirilli, J.A., Altman, R.B., and Herschlag, D. (2007) "Structural inference of native and partially folded RNA by high throughput contact mapping", Proceedings of the National Academy of Sciences U.S.A 105, 4144-4149.